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Towards Daily High-resolution Inundation Observations using Deep Learning and EO
Satellite remote sensing presents a cost-effective solution for synoptic flood monitoring, and satellite-derived flood maps provide a computationally efficient alternative to numerical flood inundation models traditionally used.
Tropical Land Use Land Cover Mapping in Pará (Brazil) using Discriminative Markov Random Fields and Multi-temporal TerraSAR-X Data
Remote sensing satellite data offer the unique possibility to map land use land cover transformations by providing spatially explicit information.
Incremental Import Vector Machines for Classifying Hyperspectral Data
The performance of the IVM in comparison to support vector machines (SVM) is evaluated in terms of accuracy and experiments are conducted to assess the potential of the probabilistic outputs of the IVM.
Shapelet-based Sparse Representation for Landcover Classification of Hyperspectral Images
A combination of shapelets and spectral information are represented in an undercomplete spatial-spectral dictionary for each individual patch, where the elements of the dictionary are linearly combined to a sparse representation of the patch.
Classification Of Hyperspectral Images
General Classification
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